Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.

While the world continues to grapple with the devastating effects of the SARS-nCoV-2 virus, different scientific groups, including researchers from different parts of the world, are trying to collaborate to discover solutions to prevent the spread of the COVID-19 virus permanently. Henceforth, the current study envisions the analysis of predictive models that employ machine learning techniques and mathematical modeling to mitigate the spread of COVID-19. A systematic literature review (SLR) has been conducted, wherein a search into different databases, viz., PubMed and IEEE Explore, fetched 1178 records initially. From an initial of 1178 records, only 50 articles were analyzed completely. Around (64%) of the studies employed data-driven mathematical models, whereas only (26%) used machine learning models. Hybrid and ARIMA models constituted about (5%) and (3%) of the selected articles. Various Quality Evaluation Metrics (QEM), including accuracy, precision, specificity, sensitivity, Brier-score, F1-score, RMSE, AUC, and prediction and validation cohort, were used to gauge the effectiveness of the studied models. The study also considered the impact of Pfizer-BioNTech (BNT162b2), AstraZeneca (ChAd0x1), and Moderna (mRNA-1273) on Beta (B.1.1.7) and Delta (B.1.617.2) viral variants and the impact of administering booster doses given the evolution of viral variants of the virus.

Blindness in children at the Ida Rieu school for the blind and deaf.

OBJECTIVE: To identify the causes of blindness at the Ida Rieu school for the blind and deaf, Karachi, Pakistan. METHODS: A cross sectional study was conducted at the Ida Rieu School for the blind and deaf. The data collected from medical record of students was entered into the WHO/PBL eye examination form for children with blindness and low vision. RESULTS: Records of 144 pupils aged between 4-30 years were reviewed, including 67% males and 33% females. One third (31%) children had visual impairment (< 6/18-6/60) and 69% were blind (< 3/60-NPL). The commonest anatomical site was retina (41%) and whole globe (20%). The etiology was unknown in 49% cases. In 33% of cases, the data suggested hereditary cause as the etiology, 40% of cases were preventable and 13% treatable. CONCLUSION: Avoidable causes of blindness were seenin 53% of children, 58% of which were preventable and 19 were treatable.